dynamic interval temporal logic - cs 135 · dynamic interval temporal logic james pustejovsky...
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![Page 1: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/1.jpg)
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Event Structure
Dynamic Interval Temporal Logic
James PustejovskyBrandeis University
November 17, 2017
Pustejovsky DITL
![Page 2: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/2.jpg)
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Event Structure
Lecture 3: Event Structure
Events as Structured Objects
Event TypesStatesTransitionsPoint VerbsProcesses
Events as Labeled Transition Systems
Dynamic Event Models
Lab on detection of event types properties in corpora
Pustejovsky DITL
![Page 3: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/3.jpg)
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Event Structure
Aktionsarten – conceptual categories of event types
Stative vs. Non-stative
States -Conceived of as not changing over time, as well asextended in time and permanent.
(1) a. John is tall.b. Mary knows the answer.c. It is 8:00 p.m.d. ! John is being tall.
Generally only compatible with simple present, but notice extendeduse of progressive and subtle meaning differences:
(2) . a. The statue stands in the square.b. The statue is standing in the square.
Structural vs. Phenomenal distinction – Goldsmith andWoisetschlager (1979)
Pustejovsky DITL
![Page 4: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/4.jpg)
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Event Structure
Aktionsarten – conceptual categories of event types
Stative vs. Non-stative
States -Conceived of as not changing over time, as well asextended in time and permanent.
(3) a. John is tall.b. Mary knows the answer.c. It is 8:00 p.m.d. ! John is being tall.
Generally only compatible with simple present, but notice extendeduse of progressive and subtle meaning differences:
(4) . a. The statue stands in the square.b. The statue is standing in the square.
Structural vs. Phenomenal distinction – Goldsmith andWoisetschlager (1979)
Pustejovsky DITL
![Page 5: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/5.jpg)
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Event Structure
Aktionsarten – conceptual categories of event types
Stative vs. Non-stative
States -Conceived of as not changing over time, as well asextended in time and permanent.
(5) a. John is tall.b. Mary knows the answer.c. It is 8:00 p.m.d. ! John is being tall.
Generally only compatible with simple present, but notice extendeduse of progressive and subtle meaning differences:
(6) . a. The statue stands in the square.b. The statue is standing in the square.
Structural vs. Phenomenal distinction – Goldsmith andWoisetschlager (1979)
Pustejovsky DITL
![Page 6: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/6.jpg)
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Event Structure
Aktionsarten – conceptual categories of event types
Stative vs. Non-stative
States -Conceived of as not changing over time, as well asextended in time and permanent.
(7) a. John is tall.b. Mary knows the answer.c. It is 8:00 p.m.d. ! John is being tall.
Generally only compatible with simple present, but notice extendeduse of progressive and subtle meaning differences:
(8) . a. The statue stands in the square.b. The statue is standing in the square.
Structural vs. Phenomenal distinction – Goldsmith andWoisetschlager (1979)
Pustejovsky DITL
![Page 7: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/7.jpg)
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Event Structure
Aktionsarten – conceptual categories of event types
Stative vs. Non-stative
States -Conceived of as not changing over time, as well asextended in time and permanent.
(9) a. John is tall.b. Mary knows the answer.c. It is 8:00 p.m.d. ! John is being tall.
Generally only compatible with simple present, but notice extendeduse of progressive and subtle meaning differences:
(10) . a. The statue stands in the square.b. The statue is standing in the square.
Structural vs. Phenomenal distinction – Goldsmith andWoisetschlager (1979)
Pustejovsky DITL
![Page 8: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/8.jpg)
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Event Structure
Temporary vs. permanent states
As seen with the English progressive marking before, states are notalways permanent. Other languages also mark these differences(but not always for the same concepts).
Spanish – ser vs. estar
(11) a. Soy enfermo (I am a sickly person)b. Estoy enfermo (if I have a cold)
Pustejovsky DITL
![Page 9: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/9.jpg)
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Event Structure
Temporary vs. permanent states
As seen with the English progressive marking before, states are notalways permanent. Other languages also mark these differences(but not always for the same concepts).
Spanish – ser vs. estar
(12) a. Soy enfermo (I am a sickly person)b. Estoy enfermo (if I have a cold)
Pustejovsky DITL
![Page 10: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/10.jpg)
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Event Structure
Processes
Involve change and are extended in time. In present tensethey need to be used in the progressive (unless habitual)
(13) . a. John ran a mile in under four minutes.b. Sheila wrote three letters in an hour.c. !John ran a mile for six minutes.d. !Sheila ate an apple for ten minutes.
(14) a. John ran for twenty minutes.b. Sheila ate apples for two days straight.c. !John ran in twenty minutes.d. !Sheila ate apples in two days.
Pustejovsky DITL
![Page 11: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/11.jpg)
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Event Structure
Processes
Involve change and are extended in time. In present tensethey need to be used in the progressive (unless habitual)
(15) . a. John ran a mile in under four minutes.b. Sheila wrote three letters in an hour.c. !John ran a mile for six minutes.d. !Sheila ate an apple for ten minutes.
(16) a. John ran for twenty minutes.b. Sheila ate apples for two days straight.c. !John ran in twenty minutes.d. !Sheila ate apples in two days.
Pustejovsky DITL
![Page 12: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/12.jpg)
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Event Structure
Processes
Involve change and are extended in time. In present tensethey need to be used in the progressive (unless habitual)
(17) . a. John ran a mile in under four minutes.b. Sheila wrote three letters in an hour.c. !John ran a mile for six minutes.d. !Sheila ate an apple for ten minutes.
(18) a. John ran for twenty minutes.b. Sheila ate apples for two days straight.c. !John ran in twenty minutes.d. !Sheila ate apples in two days.
Pustejovsky DITL
![Page 13: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/13.jpg)
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Event Structure
Distinguishing Processes from Transitions
Activities: Atelic i.e. have no natural endpoint or goal (e.g.I’m running in the park) Compatible with a durative adverbial(e.g. for) that profiles the amount of time the activity takes.
Accomplishments: Telic i.e. have a natural endpoint of goal(e.g. I’m running a mile) Compatible with a containeradverbial (e.g. in) that profiles the amount of time taken toreach the desired goal.
Pustejovsky DITL
![Page 14: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/14.jpg)
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Event Structure
Distinguishing Processes from Transitions
Activities: Atelic i.e. have no natural endpoint or goal (e.g.I’m running in the park) Compatible with a durative adverbial(e.g. for) that profiles the amount of time the activity takes.
Accomplishments: Telic i.e. have a natural endpoint of goal(e.g. I’m running a mile) Compatible with a containeradverbial (e.g. in) that profiles the amount of time taken toreach the desired goal.
Pustejovsky DITL
![Page 15: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/15.jpg)
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Event Structure
Typological Effects
Some languages are more systematic than English in distinguishingindicators of actual and potential terminal points. Thus Swedishuse different prepositions:
(19) Jeg reser till Frankrike pa tva manader.I(’m) going to France for two months.
(20) Jeg reste i Frankrike i tva manader.I traveled in France for two months.
Pustejovsky DITL
![Page 16: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/16.jpg)
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Event Structure
Typological Effects
Some languages are more systematic than English in distinguishingindicators of actual and potential terminal points. Thus Swedishuse different prepositions:
(21) Jeg reser till Frankrike pa tva manader.I(’m) going to France for two months.
(22) Jeg reste i Frankrike i tva manader.I traveled in France for two months.
Pustejovsky DITL
![Page 17: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/17.jpg)
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Event Structure
Achievements and points
Achievements: Events that are conceived of as instantaneous.Often, however, there is an underlying activity that causes achange of state. Their point-like nature tends to require them tobe described in the past tense or narrative present.
(23) a. John shattered the window.b. ! John shatters/is shattering the window.c. The canals froze.d. Mary found her keys.e. *Mary is finding her keys.f. John reached the top.
Pustejovsky DITL
![Page 18: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/18.jpg)
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Event Structure
Achievements and points
Achievements: Events that are conceived of as instantaneous.Often, however, there is an underlying activity that causes achange of state. Their point-like nature tends to require them tobe described in the past tense or narrative present.
(24) a. John shattered the window.b. ! John shatters/is shattering the window.c. The canals froze.d. Mary found her keys.e. *Mary is finding her keys.f. John reached the top.
Pustejovsky DITL
![Page 19: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/19.jpg)
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Event Structure
Achievements and points
Points: Similar to achievements in being conceived asinstantaneous, but without the underlying run-up activity thatcharacterizes gradual achievements
(25) a. Bill coughed.b. The light flashed.c. Bill is coughing.d. The light is flashing.
(c) and (d) have an iterative interpretation. Compare with thegradual achievements John is reaching the top or The canals arefreezing.
Pustejovsky DITL
![Page 20: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/20.jpg)
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Event Structure
Achievements and points
Points: Similar to achievements in being conceived asinstantaneous, but without the underlying run-up activity thatcharacterizes gradual achievements
(26) a. Bill coughed.b. The light flashed.c. Bill is coughing.d. The light is flashing.
(c) and (d) have an iterative interpretation. Compare with thegradual achievements John is reaching the top or The canals arefreezing.
Pustejovsky DITL
![Page 21: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/21.jpg)
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Event Structure
Achievements and points
Points: Similar to achievements in being conceived asinstantaneous, but without the underlying run-up activity thatcharacterizes gradual achievements
(27) a. Bill coughed.b. The light flashed.c. Bill is coughing.d. The light is flashing.
(c) and (d) have an iterative interpretation. Compare with thegradual achievements John is reaching the top or The canals arefreezing.
Pustejovsky DITL
![Page 22: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/22.jpg)
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Event Structure
Vendler Event Classes + Semelfactive
state: John loves his mother.
activity: Mary played in the park for an hour.
accomplishment: Mary wrote a novel.
achievement: John found a Euro on the floor.
point: John knocked on the door (for 2 minutes).
Pustejovsky DITL
![Page 23: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/23.jpg)
9/78
Event Structure
Vendler Event Classes + Semelfactive
state: John loves his mother.
activity: Mary played in the park for an hour.
accomplishment: Mary wrote a novel.
achievement: John found a Euro on the floor.
point: John knocked on the door (for 2 minutes).
Pustejovsky DITL
![Page 24: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/24.jpg)
9/78
Event Structure
Vendler Event Classes + Semelfactive
state: John loves his mother.
activity: Mary played in the park for an hour.
accomplishment: Mary wrote a novel.
achievement: John found a Euro on the floor.
point: John knocked on the door (for 2 minutes).
Pustejovsky DITL
![Page 25: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/25.jpg)
9/78
Event Structure
Vendler Event Classes + Semelfactive
state: John loves his mother.
activity: Mary played in the park for an hour.
accomplishment: Mary wrote a novel.
achievement: John found a Euro on the floor.
point: John knocked on the door (for 2 minutes).
Pustejovsky DITL
![Page 26: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/26.jpg)
9/78
Event Structure
Vendler Event Classes + Semelfactive
state: John loves his mother.
activity: Mary played in the park for an hour.
accomplishment: Mary wrote a novel.
achievement: John found a Euro on the floor.
point: John knocked on the door (for 2 minutes).
Pustejovsky DITL
![Page 27: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/27.jpg)
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Event Structure
Bach Eventuality Typology (Bach, 1986)
Pustejovsky DITL
![Page 28: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/28.jpg)
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Event Structure
Event Transition Graph (Moens and Steedman 1988)
Pustejovsky DITL
![Page 29: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/29.jpg)
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Event Structure
Incremental Theme Verbs
Certain NP’s measure out the event. They are direct objectsconsumed or created in increments over time (cf. eat an applevs. push a chart) (Tenny 1994).
In Mary drank a glass of wine “every part of the glass of winebeing drunk corresponds to a part of the drinking event”(Krifka 1992)
“Incremental themes are arguments that are completelyprocessed only upon termination of the event, i.e., at its endpoint” (Dowty 1991).
Pustejovsky DITL
![Page 30: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/30.jpg)
12/78
Event Structure
Incremental Theme Verbs
Certain NP’s measure out the event. They are direct objectsconsumed or created in increments over time (cf. eat an applevs. push a chart) (Tenny 1994).
In Mary drank a glass of wine “every part of the glass of winebeing drunk corresponds to a part of the drinking event”(Krifka 1992)
“Incremental themes are arguments that are completelyprocessed only upon termination of the event, i.e., at its endpoint” (Dowty 1991).
Pustejovsky DITL
![Page 31: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/31.jpg)
12/78
Event Structure
Incremental Theme Verbs
Certain NP’s measure out the event. They are direct objectsconsumed or created in increments over time (cf. eat an applevs. push a chart) (Tenny 1994).
In Mary drank a glass of wine “every part of the glass of winebeing drunk corresponds to a part of the drinking event”(Krifka 1992)
“Incremental themes are arguments that are completelyprocessed only upon termination of the event, i.e., at its endpoint” (Dowty 1991).
Pustejovsky DITL
![Page 32: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/32.jpg)
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Event Structure
Degree Achievements
Verbs with variable aspectual behavior: they seems to bechange of state verbs like other achievements , but allowdurational adverbs (Dowty 1979, Hay, Kennedy and Levin1999, Rappaport Hovav 2008).
No implication that exactly the same change of state tookplace over and over again (no semelfactives).
Scalar predicates: verbs which lexically specify a change alonga scale inasmuch as they denote an ordered set of values for aproperty of an event argument (Hay, Kennedy and Levin 1999,Rappaport Hovav 2008).
For example cool, age, lenghten, shorten; descend.
Let the soup cool for 10 minutes.
I went on working until the soup cooled.
Pustejovsky DITL
![Page 33: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/33.jpg)
13/78
Event Structure
Degree Achievements
Verbs with variable aspectual behavior: they seems to bechange of state verbs like other achievements , but allowdurational adverbs (Dowty 1979, Hay, Kennedy and Levin1999, Rappaport Hovav 2008).
No implication that exactly the same change of state tookplace over and over again (no semelfactives).
Scalar predicates: verbs which lexically specify a change alonga scale inasmuch as they denote an ordered set of values for aproperty of an event argument (Hay, Kennedy and Levin 1999,Rappaport Hovav 2008).
For example cool, age, lenghten, shorten; descend.
Let the soup cool for 10 minutes.
I went on working until the soup cooled.
Pustejovsky DITL
![Page 34: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/34.jpg)
13/78
Event Structure
Degree Achievements
Verbs with variable aspectual behavior: they seems to bechange of state verbs like other achievements , but allowdurational adverbs (Dowty 1979, Hay, Kennedy and Levin1999, Rappaport Hovav 2008).
No implication that exactly the same change of state tookplace over and over again (no semelfactives).
Scalar predicates: verbs which lexically specify a change alonga scale inasmuch as they denote an ordered set of values for aproperty of an event argument (Hay, Kennedy and Levin 1999,Rappaport Hovav 2008).
For example cool, age, lenghten, shorten; descend.
Let the soup cool for 10 minutes.
I went on working until the soup cooled.
Pustejovsky DITL
![Page 35: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/35.jpg)
13/78
Event Structure
Degree Achievements
Verbs with variable aspectual behavior: they seems to bechange of state verbs like other achievements , but allowdurational adverbs (Dowty 1979, Hay, Kennedy and Levin1999, Rappaport Hovav 2008).
No implication that exactly the same change of state tookplace over and over again (no semelfactives).
Scalar predicates: verbs which lexically specify a change alonga scale inasmuch as they denote an ordered set of values for aproperty of an event argument (Hay, Kennedy and Levin 1999,Rappaport Hovav 2008).
For example cool, age, lenghten, shorten; descend.
Let the soup cool for 10 minutes.
I went on working until the soup cooled.
Pustejovsky DITL
![Page 36: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/36.jpg)
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Event Structure
Points
Moens and Steedman 1988 analyze point expressions as thosethat are not normally associated to a consequent state(consequent state defined as no transition to a new state inthe world – according to Moens and Steedman a point is anevent whose consequences are not at issue in the discourse).
Semelfactives (Smith 1990, Rothstein 2004).
*arrived/landed for five minutes, knocked/tapped for fiveminutes.
Points admit iterative readings under coercive contexts(Moens and Steedman 1988).
Pustejovsky DITL
![Page 37: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/37.jpg)
14/78
Event Structure
Points
Moens and Steedman 1988 analyze point expressions as thosethat are not normally associated to a consequent state(consequent state defined as no transition to a new state inthe world – according to Moens and Steedman a point is anevent whose consequences are not at issue in the discourse).
Semelfactives (Smith 1990, Rothstein 2004).
*arrived/landed for five minutes, knocked/tapped for fiveminutes.
Points admit iterative readings under coercive contexts(Moens and Steedman 1988).
Pustejovsky DITL
![Page 38: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/38.jpg)
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Event Structure
Points
Moens and Steedman 1988 analyze point expressions as thosethat are not normally associated to a consequent state(consequent state defined as no transition to a new state inthe world – according to Moens and Steedman a point is anevent whose consequences are not at issue in the discourse).
Semelfactives (Smith 1990, Rothstein 2004).
*arrived/landed for five minutes, knocked/tapped for fiveminutes.
Points admit iterative readings under coercive contexts(Moens and Steedman 1988).
Pustejovsky DITL
![Page 39: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/39.jpg)
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Event Structure
Points
Moens and Steedman 1988 analyze point expressions as thosethat are not normally associated to a consequent state(consequent state defined as no transition to a new state inthe world – according to Moens and Steedman a point is anevent whose consequences are not at issue in the discourse).
Semelfactives (Smith 1990, Rothstein 2004).
*arrived/landed for five minutes, knocked/tapped for fiveminutes.
Points admit iterative readings under coercive contexts(Moens and Steedman 1988).
Pustejovsky DITL
![Page 40: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/40.jpg)
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Event Structure
Aspectual Composition
Bare plurals and mass-terms arguments can make a sentencewith a telic predicate behave as if it were ’durative’ or’imperfective’ in aspect (Verkuyl 1972).
John drank a glass of beer (perfective).
John drank beer (imperfective).
Pustejovsky DITL
![Page 41: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/41.jpg)
15/78
Event Structure
Aspectual Composition
Bare plurals and mass-terms arguments can make a sentencewith a telic predicate behave as if it were ’durative’ or’imperfective’ in aspect (Verkuyl 1972).
John drank a glass of beer (perfective).
John drank beer (imperfective).
Pustejovsky DITL
![Page 42: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/42.jpg)
15/78
Event Structure
Aspectual Composition
Bare plurals and mass-terms arguments can make a sentencewith a telic predicate behave as if it were ’durative’ or’imperfective’ in aspect (Verkuyl 1972).
John drank a glass of beer (perfective).
John drank beer (imperfective).
Pustejovsky DITL
![Page 43: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/43.jpg)
15/78
Event Structure
Aspectual Composition
Bare plurals and mass-terms arguments can make a sentencewith a telic predicate behave as if it were ’durative’ or’imperfective’ in aspect (Verkuyl 1972).
John drank a glass of beer (perfective).
John drank beer (imperfective).
Pustejovsky DITL
![Page 44: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/44.jpg)
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Event Structure
Aspectual Coercion
“A person leads somebody somewhere” (PROCESS) vs. “Aroad leads somewhere” (STATE)
“An object falls to the ground” (TRANSITION) vs. “A casefalls into a certain category” (STATE)
Pustejovsky DITL
![Page 45: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/45.jpg)
16/78
Event Structure
Aspectual Coercion
“A person leads somebody somewhere” (PROCESS) vs. “Aroad leads somewhere” (STATE)
“An object falls to the ground” (TRANSITION) vs. “A casefalls into a certain category” (STATE)
Pustejovsky DITL
![Page 46: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/46.jpg)
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Event Structure
Subatomic Event StructurePustejovsky (1991)
(28) a. event → state ∣ process ∣ transition
b. state: → ec. process: → e1 . . . end. transitionach: → state statee. transitionacc : → process state
Pustejovsky DITL
![Page 47: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/47.jpg)
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Event Structure
Subatomic Event StructurePustejovsky (1991)
(29) a. event → state ∣ process ∣ transitionb. state: → e
c. process: → e1 . . . end. transitionach: → state statee. transitionacc : → process state
Pustejovsky DITL
![Page 48: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/48.jpg)
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Event Structure
Subatomic Event StructurePustejovsky (1991)
(30) a. event → state ∣ process ∣ transitionb. state: → ec. process: → e1 . . . en
d. transitionach: → state statee. transitionacc : → process state
Pustejovsky DITL
![Page 49: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/49.jpg)
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Event Structure
Subatomic Event StructurePustejovsky (1991)
(31) a. event → state ∣ process ∣ transitionb. state: → ec. process: → e1 . . . end. transitionach: → state state
e. transitionacc : → process state
Pustejovsky DITL
![Page 50: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/50.jpg)
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Event Structure
Subatomic Event StructurePustejovsky (1991)
(32) a. event → state ∣ process ∣ transitionb. state: → ec. process: → e1 . . . end. transitionach: → state statee. transitionacc : → process state
Pustejovsky DITL
![Page 51: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/51.jpg)
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Event Structure
Qualia Structure for CausativePustejovsky (1995)
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
kill
eventstr =
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
e1 = e1:processe2 = e2:stateRestr = <∝Head = e1
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
argstr =
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
arg1 = 1
⎡⎢⎢⎢⎢⎢⎣
indformal = physobj
⎤⎥⎥⎥⎥⎥⎦
arg2 = 2
⎡⎢⎢⎢⎢⎢⎣
animate indformal = physobj
⎤⎥⎥⎥⎥⎥⎦
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
qualia =
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎣
cause-lcpformal = dead(e2, 2 )agentive = kill act(e1, 1 , 2 )
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎦
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
Pustejovsky DITL
![Page 52: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/52.jpg)
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Event Structure
Opposition StructurePustejovsky (2000)
(33) kille
<������
HHHHHH
e2
dead(y)
e∗1
kill act(x , y)¬dead(y)
(34) breake
<������
HHHHHH
e2
broken(y)
e1
break act(x , y)¬broken(y)
Pustejovsky DITL
![Page 53: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/53.jpg)
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Event Structure
Qualia Structure with Opposition Structure
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
kill
eventstr =
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
e0 = e0:statee1 = e1:processe2 = e2:stateRestr = <∝Head = e1
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
argstr =
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
arg1 = 1
⎡⎢⎢⎢⎢⎢⎣
indformal = physobj
⎤⎥⎥⎥⎥⎥⎦
arg2 = 2
⎡⎢⎢⎢⎢⎢⎣
animate indformal = physobj
⎤⎥⎥⎥⎥⎥⎦
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
qualia =
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
cause-lcpformal = dead(e2, 2 )agentive = kill act(e1, 1 , 2 )precond = ¬dead(e0, 2 )
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
Pustejovsky DITL
![Page 54: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/54.jpg)
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Event Structure
Opposition is Part of Event StructureeHHHHH
�����
<
e1kill act(x , y)
e2
¬dead(w)P
HHH
HH
dead(w)P
�����
OSe<HH
HH��
��e1○
e1
HHH
���
kill act(x , y)
e3
¬dead(y)
e2
dead(y)
Pustejovsky DITL
![Page 55: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/55.jpg)
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Event Structure
Dynamic Extensions to GL
Qualia Structure: Can be interpreted dynamically
Dynamic Selection: Encodes the way an argument participatesin the event
Tracking change: Models the dynamics of participantattributes
Pustejovsky DITL
![Page 56: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/56.jpg)
22/78
Event Structure
Dynamic Extensions to GL
Qualia Structure: Can be interpreted dynamically
Dynamic Selection: Encodes the way an argument participatesin the event
Tracking change: Models the dynamics of participantattributes
Pustejovsky DITL
![Page 57: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/57.jpg)
22/78
Event Structure
Dynamic Extensions to GL
Qualia Structure: Can be interpreted dynamically
Dynamic Selection: Encodes the way an argument participatesin the event
Tracking change: Models the dynamics of participantattributes
Pustejovsky DITL
![Page 58: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/58.jpg)
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Event Structure
Inherent Dynamic Aspect of Qualia Structure
Parameters of a verb, P, extend over sequential frames ofinterpretation (subevents).
P is decomposed into different subpredicates within theseevents:
Verb(Arg1Arg2) Ô⇒ λyλx P1(x , y)A
P2(y)F
Pustejovsky DITL
![Page 59: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/59.jpg)
23/78
Event Structure
Inherent Dynamic Aspect of Qualia Structure
Parameters of a verb, P, extend over sequential frames ofinterpretation (subevents).
P is decomposed into different subpredicates within theseevents:
Verb(Arg1Arg2) Ô⇒ λyλx P1(x , y)A
P2(y)F
Pustejovsky DITL
![Page 60: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/60.jpg)
23/78
Event Structure
Inherent Dynamic Aspect of Qualia Structure
Parameters of a verb, P, extend over sequential frames ofinterpretation (subevents).
P is decomposed into different subpredicates within theseevents:
Verb(Arg1Arg2) Ô⇒ λyλx P1(x , y)A
P2(y)F
Pustejovsky DITL
![Page 61: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/61.jpg)
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Event Structure
Frame-based Event Structure
Φ ¬Φ
Φ
Φ/p Φ/¬p Φ/p Φ/¬p+
State (S)
DerivedTransition
Transition (T)
Process (P)
Φ/p Φ/¬p Φ/p Φ/¬p+
ΦP(x)
¬Φ¬P(x)
2nd Conference on CTF, Pustejovsky (2009)
Pustejovsky DITL
![Page 62: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/62.jpg)
25/78
Event Structure
Dynamic Event Structure
Events are built up from multiple (stacked) layers of primitiveconstraints on the individual participants.
There may be many changes taking place within one atomicevent, when viewed at the subatomic level.
Pustejovsky DITL
![Page 63: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/63.jpg)
25/78
Event Structure
Dynamic Event Structure
Events are built up from multiple (stacked) layers of primitiveconstraints on the individual participants.
There may be many changes taking place within one atomicevent, when viewed at the subatomic level.
Pustejovsky DITL
![Page 64: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/64.jpg)
26/78
Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:1 They can be ordered, α;β ( α is followed by β);2 They can be iterated, a∗ (apply a zero or more times);3 They can be disjoined, α ∪ β (apply either α or β);4 They can be turned into formulas
[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 65: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/65.jpg)
26/78
Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:1 They can be ordered, α;β ( α is followed by β);2 They can be iterated, a∗ (apply a zero or more times);3 They can be disjoined, α ∪ β (apply either α or β);4 They can be turned into formulas
[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 66: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/66.jpg)
26/78
Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:1 They can be ordered, α;β ( α is followed by β);2 They can be iterated, a∗ (apply a zero or more times);3 They can be disjoined, α ∪ β (apply either α or β);4 They can be turned into formulas
[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 67: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/67.jpg)
26/78
Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:
1 They can be ordered, α;β ( α is followed by β);2 They can be iterated, a∗ (apply a zero or more times);3 They can be disjoined, α ∪ β (apply either α or β);4 They can be turned into formulas
[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 68: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/68.jpg)
26/78
Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:1 They can be ordered, α;β ( α is followed by β);
2 They can be iterated, a∗ (apply a zero or more times);3 They can be disjoined, α ∪ β (apply either α or β);4 They can be turned into formulas
[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 69: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/69.jpg)
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Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:1 They can be ordered, α;β ( α is followed by β);2 They can be iterated, a∗ (apply a zero or more times);
3 They can be disjoined, α ∪ β (apply either α or β);4 They can be turned into formulas
[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 70: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/70.jpg)
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Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:1 They can be ordered, α;β ( α is followed by β);2 They can be iterated, a∗ (apply a zero or more times);3 They can be disjoined, α ∪ β (apply either α or β);
4 They can be turned into formulas[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 71: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/71.jpg)
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Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:1 They can be ordered, α;β ( α is followed by β);2 They can be iterated, a∗ (apply a zero or more times);3 They can be disjoined, α ∪ β (apply either α or β);4 They can be turned into formulas
[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 72: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/72.jpg)
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Event Structure
Dynamic Interval Temporal Logic
(Pustejovsky and Moszkowicz, 2011)
Formulas: φ propositions. Evaluated in a state, s.
Programs: α, functions from states to states, s × s. Evaluatedover a pair of states, (s, s ′).
Temporal Operators: ◯φ, 3φ, 2φ, φ Uψ.
Program composition:1 They can be ordered, α;β ( α is followed by β);2 They can be iterated, a∗ (apply a zero or more times);3 They can be disjoined, α ∪ β (apply either α or β);4 They can be turned into formulas
[α]φ (after every execution of α, φ is true);⟨α⟩φ (there is an execution of α, such that φ is true);
5 Formulas can become programs, φ? (test to see if φ is true,and proceed if so).
Pustejovsky DITL
![Page 73: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/73.jpg)
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Event Structure
Dynamic Event Structure
(35) a. Mary was sick today.b. My phone was expensive.c. Sam lives in Boston.
We assume that a state is defined as a single frame structure(event), containing a proposition, where the frame is temporallyindexed, i.e., e i → φ is interpreted as φ holding as true at time i .The frame-based representation from Pustejovsky and Moszkowicz(2011) can be given as follows:
Pustejovsky DITL
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Event Structure
Dynamic Event Structure
(36) a. Mary was sick today.b. My phone was expensive.c. Sam lives in Boston.
We assume that a state is defined as a single frame structure(event), containing a proposition, where the frame is temporallyindexed, i.e., e i → φ is interpreted as φ holding as true at time i .The frame-based representation from Pustejovsky and Moszkowicz(2011) can be given as follows:
Pustejovsky DITL
![Page 75: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/75.jpg)
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Event Structure
Dynamic Event Structure
(37) φi
e
Propositions can be evaluated over subsequent states, of course, sowe need an operation of concatenation, +, which applies to two ormore event frames, as illustrated below.
(38) φi
e+ φ
j
e= φ
[i ,j]
e
Semantic interpretations for these are:
(39) a. [[ φ ]]M,i = 1 iff VM,i(φ) = 1.
b. [[ φ φ ]]M,⟨i ,j⟩ = 1 iff VM,(φ) = 1 and VM,j(φ) = 1,where i < j .
Pustejovsky DITL
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Event Structure
Dynamic Event Structure
(40) φi
e
Propositions can be evaluated over subsequent states, of course, sowe need an operation of concatenation, +, which applies to two ormore event frames, as illustrated below.
(41) φi
e+ φ
j
e= φ
[i ,j]
e
Semantic interpretations for these are:
(42) a. [[ φ ]]M,i = 1 iff VM,i(φ) = 1.
b. [[ φ φ ]]M,⟨i ,j⟩ = 1 iff VM,(φ) = 1 and VM,j(φ) = 1,where i < j .
Pustejovsky DITL
![Page 77: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/77.jpg)
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Event Structure
Dynamic Event Structure
(43) φi
e
Propositions can be evaluated over subsequent states, of course, sowe need an operation of concatenation, +, which applies to two ormore event frames, as illustrated below.
(44) φi
e+ φ
j
e= φ
[i ,j]
e
Semantic interpretations for these are:
(45) a. [[ φ ]]M,i = 1 iff VM,i(φ) = 1.
b. [[ φ φ ]]M,⟨i ,j⟩ = 1 iff VM,(φ) = 1 and VM,j(φ) = 1,where i < j .
Pustejovsky DITL
![Page 78: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/78.jpg)
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Event Structure
Dynamic Event Structure
(46) φi
e
Propositions can be evaluated over subsequent states, of course, sowe need an operation of concatenation, +, which applies to two ormore event frames, as illustrated below.
(47) φi
e+ φ
j
e= φ
[i ,j]
e
Semantic interpretations for these are:
(48) a. [[ φ ]]M,i = 1 iff VM,i(φ) = 1.
b. [[ φ φ ]]M,⟨i ,j⟩ = 1 iff VM,(φ) = 1 and VM,j(φ) = 1,where i < j .
Pustejovsky DITL
![Page 79: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/79.jpg)
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Event Structure
Dynamic Event Structure
(49) φi
e
Propositions can be evaluated over subsequent states, of course, sowe need an operation of concatenation, +, which applies to two ormore event frames, as illustrated below.
(50) φi
e+ φ
j
e= φ
[i ,j]
e
Semantic interpretations for these are:
(51) a. [[ φ ]]M,i = 1 iff VM,i(φ) = 1.
b. [[ φ φ ]]M,⟨i ,j⟩ = 1 iff VM,(φ) = 1 and VM,j(φ) = 1,where i < j .
Pustejovsky DITL
![Page 80: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/80.jpg)
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Event Structure
Dynamic Event Structure
(52) e i
φ
Tree structure for event concatenation:
e i
φ
+e j
φ
=e[i ,j]
φ
Pustejovsky DITL
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Event Structure
Dynamic Event Structure
(53) e i
φ
Tree structure for event concatenation:
e i
φ
+e j
φ
=e[i ,j]
φ
Pustejovsky DITL
![Page 82: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/82.jpg)
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Event Structure
Labeled Transition System (LTS)
The dynamics of actions can be modeled as a Labeled TransitionSystems (LTS).
An LTS consists of a 3-tuple, ⟨S ,Act,→⟩, where
(54) a. S is the set of states;b. Act is a set of actions;c. → is a total transition relation: →⊆ S ×Act × S .
(55) (e1, α, e2) ∈→
cf. Fernando (2001, 2013)
Pustejovsky DITL
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Event Structure
Labeled Transition System (LTS)
The dynamics of actions can be modeled as a Labeled TransitionSystems (LTS).
An LTS consists of a 3-tuple, ⟨S ,Act,→⟩, where
(56) a. S is the set of states;b. Act is a set of actions;c. → is a total transition relation: →⊆ S ×Act × S .
(57) (e1, α, e2) ∈→
cf. Fernando (2001, 2013)
Pustejovsky DITL
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Event Structure
Labeled Transition System (LTS)
The dynamics of actions can be modeled as a Labeled TransitionSystems (LTS).
An LTS consists of a 3-tuple, ⟨S ,Act,→⟩, where
(58) a. S is the set of states;b. Act is a set of actions;c. → is a total transition relation: →⊆ S ×Act × S .
(59) (e1, α, e2) ∈→
cf. Fernando (2001, 2013)
Pustejovsky DITL
![Page 85: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/85.jpg)
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Event Structure
Labeled Transition System (LTS)
The dynamics of actions can be modeled as a Labeled TransitionSystems (LTS).
An LTS consists of a 3-tuple, ⟨S ,Act,→⟩, where
(60) a. S is the set of states;b. Act is a set of actions;c. → is a total transition relation: →⊆ S ×Act × S .
(61) (e1, α, e2) ∈→
cf. Fernando (2001, 2013)
Pustejovsky DITL
![Page 86: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/86.jpg)
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Event Structure
Labeled Transition System (LTS)
An action, α provides the labeling on an arrow, making it explicitwhat brings about a state-to-state transition.
As a shorthand for
(62) a. (e1, α, e2) ∈→, we will also use:
b. e1αÐ→ e3
S1 S2
p ¬pA
Pustejovsky DITL
![Page 87: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/87.jpg)
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Event Structure
Labeled Transition System (LTS)
An action, α provides the labeling on an arrow, making it explicitwhat brings about a state-to-state transition.
As a shorthand for
(63) a. (e1, α, e2) ∈→, we will also use:
b. e1αÐ→ e3
S1 S2
p ¬pA
Pustejovsky DITL
![Page 88: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/88.jpg)
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Event Structure
Labeled Transition System (LTS)
An action, α provides the labeling on an arrow, making it explicitwhat brings about a state-to-state transition.
As a shorthand for
(64) a. (e1, α, e2) ∈→, we will also use:
b. e1αÐ→ e3
S1 S2
p ¬pA
Pustejovsky DITL
![Page 89: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/89.jpg)
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Event Structure
Labeled Transition System (LTS)
An action, α provides the labeling on an arrow, making it explicitwhat brings about a state-to-state transition.
As a shorthand for
(65) a. (e1, α, e2) ∈→, we will also use:
b. e1αÐ→ e3
S1 S2
p ¬pA
Pustejovsky DITL
![Page 90: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/90.jpg)
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Event Structure
Labeled Transition System (LTS)
An action, α provides the labeling on an arrow, making it explicitwhat brings about a state-to-state transition.
As a shorthand for
(66) a. (e1, α, e2) ∈→, we will also use:
b. e1αÐ→ e3
S1 S2
p ¬pA
Pustejovsky DITL
![Page 91: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/91.jpg)
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Event Structure
Labeled Transition System (LTS)
If reference to the state content (rather than state name) isrequired for interpretation purposes, then as shorthand for:({φ}e1 , α,{¬φ}e2) ∈→, we use:
(67) φe1
αÐ→ ¬φe2
S1 S2
p ¬pA
Pustejovsky DITL
![Page 92: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/92.jpg)
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Event Structure
Labeled Transition System (LTS)
If reference to the state content (rather than state name) isrequired for interpretation purposes, then as shorthand for:({φ}e1 , α,{¬φ}e2) ∈→, we use:
(68) φe1
αÐ→ ¬φe2
S1 S2
p ¬pA
Pustejovsky DITL
![Page 93: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/93.jpg)
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Event Structure
Temporal Labeled Transition System (TLTS)
With temporal indexing from a Linear Temporal Logic, we candefine a Temporal Labeled Transition System (TLTS). For a state,e1, indexed at time i , we say e1@i .({φ}e1@i , α,{¬φ}e2@i+1) ∈→(i ,i+1), we use:
(69) φi
e1
αÐ→ ¬φi+1
e2
Pustejovsky DITL
![Page 94: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/94.jpg)
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Event Structure
Temporal Labeled Transition System (TLTS)
With temporal indexing from a Linear Temporal Logic, we candefine a Temporal Labeled Transition System (TLTS). For a state,e1, indexed at time i , we say e1@i .({φ}e1@i , α,{¬φ}e2@i+1) ∈→(i ,i+1), we use:
(70) φi
e1
αÐ→ ¬φi+1
e2
Pustejovsky DITL
![Page 95: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/95.jpg)
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Event Structure
Dynamic Event Structure
(71) e[i,i+1]HH
HHH
�����
e i1-α
e i+12
φ ¬φ
Pustejovsky DITL
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Event Structure
Dynamic Event Structure
(72) Mary awoke from a long sleep.
The state of being asleep has a duration, [i , j], who’s valuation isgated by the waking event at the “next state”, j + 1.
(73) e[i,j+1]HH
HHH
�����
e[i,j]1-α
e j+12
φ ¬φ
Pustejovsky DITL
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Event Structure
Dynamic Event Structure
(74) Mary awoke from a long sleep.
The state of being asleep has a duration, [i , j], who’s valuation isgated by the waking event at the “next state”, j + 1.
(75) e[i,j+1]HH
HHH
�����
e[i,j]1-α
e j+12
φ ¬φ
Pustejovsky DITL
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Event Structure
Dynamic Event Structure
(76) Mary awoke from a long sleep.
The state of being asleep has a duration, [i , j], who’s valuation isgated by the waking event at the “next state”, j + 1.
(77) e[i,j+1]HH
HHH
�����
e[i,j]1-α
e j+12
φ ¬φ
Pustejovsky DITL
![Page 99: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/99.jpg)
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Event Structure
Simple First-order Transition
(78) x ∶= y (ν-transition)“x assumes the value given to y in the next state.”⟨M, (i , i + 1), (u,u[x/u(y)])⟩ ⊧ x ∶= yiff ⟨M, i ,u⟩ ⊧ s1 ∧ ⟨M, i + 1,u[x/u(y)]⟩ ⊧ x = y
(79) e[i,i+1]HHHHH
�����
e i1-x ∶= y
e i+12
A(z) = x A(z) = y
Pustejovsky DITL
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Event Structure
Simple First-order Transition
(80) x ∶= y (ν-transition)“x assumes the value given to y in the next state.”⟨M, (i , i + 1), (u,u[x/u(y)])⟩ ⊧ x ∶= yiff ⟨M, i ,u⟩ ⊧ s1 ∧ ⟨M, i + 1,u[x/u(y)]⟩ ⊧ x = y
(81) e[i,i+1]HHHHH
�����
e i1-x ∶= y
e i+12
A(z) = x A(z) = y
Pustejovsky DITL
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Event Structure
Processes
With a ν-transition defined, a process can be viewed as simply aniteration of basic variable assignments and re-assignments:
(82)eHHHHH
�����
e1-ν e2 . . . -ν en
Pustejovsky DITL
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Event Structure
Processes
With a ν-transition defined, a process can be viewed as simply aniteration of basic variable assignments and re-assignments:
(83)eHHHHH
�����
e1-ν e2 . . . -ν en
Pustejovsky DITL
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Event Structure
Spatial Relations in Motion Predicates
Topological Path Expressionsarrive, leave, exit, land, take off
Orientation Path Expressionsclimb, descend
Topo-metric Path Expressionsapproach, near, distance oneself
Topo-metric orientation Expressionsjust below, just above
Pustejovsky DITL
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Event Structure
Spatial Relations in Motion Predicates
Topological Path Expressionsarrive, leave, exit, land, take off
Orientation Path Expressionsclimb, descend
Topo-metric Path Expressionsapproach, near, distance oneself
Topo-metric orientation Expressionsjust below, just above
Pustejovsky DITL
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Event Structure
Spatial Relations in Motion Predicates
Topological Path Expressionsarrive, leave, exit, land, take off
Orientation Path Expressionsclimb, descend
Topo-metric Path Expressionsapproach, near, distance oneself
Topo-metric orientation Expressionsjust below, just above
Pustejovsky DITL
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Event Structure
Spatial Relations in Motion Predicates
Topological Path Expressionsarrive, leave, exit, land, take off
Orientation Path Expressionsclimb, descend
Topo-metric Path Expressionsapproach, near, distance oneself
Topo-metric orientation Expressionsjust below, just above
Pustejovsky DITL
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Event Structure
Language Data
Manner construction languagesPath information is encoded in directional PPs and otheradjuncts, while verb encode manner of motion
English, German, Russian, Swedish, Chinese
Path construction languagesPath information is encoded in matrix verb, while adjunctsspecify manner of motionModern Greek, Spanish, Japanese, Turkish, Hindi
Pustejovsky DITL
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Event Structure
Language Data
Manner construction languagesPath information is encoded in directional PPs and otheradjuncts, while verb encode manner of motionEnglish, German, Russian, Swedish, Chinese
Path construction languages
Path information is encoded in matrix verb, while adjunctsspecify manner of motionModern Greek, Spanish, Japanese, Turkish, Hindi
Pustejovsky DITL
![Page 109: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/109.jpg)
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Event Structure
Language Data
Manner construction languagesPath information is encoded in directional PPs and otheradjuncts, while verb encode manner of motionEnglish, German, Russian, Swedish, Chinese
Path construction languagesPath information is encoded in matrix verb, while adjunctsspecify manner of motionModern Greek, Spanish, Japanese, Turkish, Hindi
Pustejovsky DITL
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Event Structure
Defining Motion (Talmy 1985)
(84) a. The event or situation involved in the change of location ;
b. The object (construed as a point or region) that isundergoing movement (the figure);c. The region (or path) traversed through the motion;d. A distinguished point or region of the path (the ground);e. The manner in which the change of location is carried out;f. The medium through which the motion takes place.
Pustejovsky DITL
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Event Structure
Defining Motion (Talmy 1985)
(85) a. The event or situation involved in the change of location ;b. The object (construed as a point or region) that isundergoing movement (the figure);
c. The region (or path) traversed through the motion;d. A distinguished point or region of the path (the ground);e. The manner in which the change of location is carried out;f. The medium through which the motion takes place.
Pustejovsky DITL
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Event Structure
Defining Motion (Talmy 1985)
(86) a. The event or situation involved in the change of location ;b. The object (construed as a point or region) that isundergoing movement (the figure);c. The region (or path) traversed through the motion;
d. A distinguished point or region of the path (the ground);e. The manner in which the change of location is carried out;f. The medium through which the motion takes place.
Pustejovsky DITL
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Event Structure
Defining Motion (Talmy 1985)
(87) a. The event or situation involved in the change of location ;b. The object (construed as a point or region) that isundergoing movement (the figure);c. The region (or path) traversed through the motion;d. A distinguished point or region of the path (the ground);
e. The manner in which the change of location is carried out;f. The medium through which the motion takes place.
Pustejovsky DITL
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Event Structure
Defining Motion (Talmy 1985)
(88) a. The event or situation involved in the change of location ;b. The object (construed as a point or region) that isundergoing movement (the figure);c. The region (or path) traversed through the motion;d. A distinguished point or region of the path (the ground);e. The manner in which the change of location is carried out;
f. The medium through which the motion takes place.
Pustejovsky DITL
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Event Structure
Defining Motion (Talmy 1985)
(89) a. The event or situation involved in the change of location ;b. The object (construed as a point or region) that isundergoing movement (the figure);c. The region (or path) traversed through the motion;d. A distinguished point or region of the path (the ground);e. The manner in which the change of location is carried out;f. The medium through which the motion takes place.
Pustejovsky DITL
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Event Structure
Manner Predicates
(90) SHHHHH
�����
NP �figure
VP
John Vact
biked
Pustejovsky DITL
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Event Structure
Path Predicates
(91) SHHHHH
�����
NP �figure
VP
John
�����
Vtrans
departed
HHHHH
NP-ground
Boston
Pustejovsky DITL
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Event Structure
Manner with Path Adjunction
(92) SHHHHH
�����
NP �figure
VP
John Vact
biked
� ground XXXXXX
XXX
PP
transto the store
Pustejovsky DITL
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Event Structure
Path with Manner Adjunction
(93) SHHHHH
�����
NP �figure
VP
John
�����
Vtrans
departed
HHHHH
NP-ground
Boston
XXXXX
XXXX
PP
actby car
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 1/2
(94) a. Isabel climbed for 15 minutes.
b. Nicholas fell 100 meters.
(95) a. There is an action (e) bringing about an iteratednon-distinguished change of location;b. The figure undergoes this non-distinguished change oflocation;c. The figure creates (leaves) a path by virtue of the motion.d. The action (e) is performed in a certain manner.e. The path is oriented in an identified or distinguished way.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 1/2
(96) a. Isabel climbed for 15 minutes.b. Nicholas fell 100 meters.
(97) a. There is an action (e) bringing about an iteratednon-distinguished change of location;b. The figure undergoes this non-distinguished change oflocation;c. The figure creates (leaves) a path by virtue of the motion.d. The action (e) is performed in a certain manner.e. The path is oriented in an identified or distinguished way.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 1/2
(98) a. Isabel climbed for 15 minutes.b. Nicholas fell 100 meters.
(99) a. There is an action (e) bringing about an iteratednon-distinguished change of location;
b. The figure undergoes this non-distinguished change oflocation;c. The figure creates (leaves) a path by virtue of the motion.d. The action (e) is performed in a certain manner.e. The path is oriented in an identified or distinguished way.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 1/2
(100) a. Isabel climbed for 15 minutes.b. Nicholas fell 100 meters.
(101) a. There is an action (e) bringing about an iteratednon-distinguished change of location;b. The figure undergoes this non-distinguished change oflocation;
c. The figure creates (leaves) a path by virtue of the motion.d. The action (e) is performed in a certain manner.e. The path is oriented in an identified or distinguished way.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 1/2
(102) a. Isabel climbed for 15 minutes.b. Nicholas fell 100 meters.
(103) a. There is an action (e) bringing about an iteratednon-distinguished change of location;b. The figure undergoes this non-distinguished change oflocation;c. The figure creates (leaves) a path by virtue of the motion.
d. The action (e) is performed in a certain manner.e. The path is oriented in an identified or distinguished way.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 1/2
(104) a. Isabel climbed for 15 minutes.b. Nicholas fell 100 meters.
(105) a. There is an action (e) bringing about an iteratednon-distinguished change of location;b. The figure undergoes this non-distinguished change oflocation;c. The figure creates (leaves) a path by virtue of the motion.d. The action (e) is performed in a certain manner.
e. The path is oriented in an identified or distinguished way.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 1/2
(106) a. Isabel climbed for 15 minutes.b. Nicholas fell 100 meters.
(107) a. There is an action (e) bringing about an iteratednon-distinguished change of location;b. The figure undergoes this non-distinguished change oflocation;c. The figure creates (leaves) a path by virtue of the motion.d. The action (e) is performed in a certain manner.e. The path is oriented in an identified or distinguished way.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 2/2
Unlike pure manner verbs, this class of predicates admits of twocompositional constructions with adjuncts.
(108) Manner of motion verb with path adjunct;John climbed to the summit.
(109) Manner of motion verb with path argument;John climbed the mountain.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 2/2
Unlike pure manner verbs, this class of predicates admits of twocompositional constructions with adjuncts.
(110) Manner of motion verb with path adjunct;John climbed to the summit.
(111) Manner of motion verb with path argument;John climbed the mountain.
Pustejovsky DITL
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Event Structure
Path+manner Predicates (Talmy 2000) 2/2
Unlike pure manner verbs, this class of predicates admits of twocompositional constructions with adjuncts.
(112) Manner of motion verb with path adjunct;John climbed to the summit.
(113) Manner of motion verb with path argument;John climbed the mountain.
Pustejovsky DITL
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Event Structure
With Path Adjunct
(114) SHHHHH
�����
NP �figure
VP
John Vact
climbed
� ground XXXXXX
XXX
PP
transto the summit
Pustejovsky DITL
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Event Structure
With Path Argument
(115) SHHHHH
�����
NP �figure
VP
John
�����
Vtrans
climbed
HHHHH
NP-path
the mountain
Pustejovsky DITL
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Event Structure
Tracking Motion with RCC8: example of enter
AA
AA A
B B B B B
DC(A,B) PO(A,B) TPP(A,B) NTPP(A,B)EC(A,B)
t1 t2 t3 t4 t5
Pustejovsky DITL
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Event Structure
Capturing Motion as Change in Spatial Relations
Dynamic Interval Temporal Logic
Path verbs designate a distinguished value in the change oflocation, from one state to another.The change in value is tested.
Manner of motion verbs iterate a change in location fromstate to state.The value is assigned and reassigned.
Pustejovsky DITL
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Event Structure
Capturing Motion as Change in Spatial Relations
Dynamic Interval Temporal Logic
Path verbs designate a distinguished value in the change oflocation, from one state to another.
The change in value is tested.
Manner of motion verbs iterate a change in location fromstate to state.The value is assigned and reassigned.
Pustejovsky DITL
![Page 135: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/135.jpg)
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Event Structure
Capturing Motion as Change in Spatial Relations
Dynamic Interval Temporal Logic
Path verbs designate a distinguished value in the change oflocation, from one state to another.The change in value is tested.
Manner of motion verbs iterate a change in location fromstate to state.The value is assigned and reassigned.
Pustejovsky DITL
![Page 136: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/136.jpg)
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Event Structure
Capturing Motion as Change in Spatial Relations
Dynamic Interval Temporal Logic
Path verbs designate a distinguished value in the change oflocation, from one state to another.The change in value is tested.
Manner of motion verbs iterate a change in location fromstate to state.
The value is assigned and reassigned.
Pustejovsky DITL
![Page 137: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/137.jpg)
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Event Structure
Capturing Motion as Change in Spatial Relations
Dynamic Interval Temporal Logic
Path verbs designate a distinguished value in the change oflocation, from one state to another.The change in value is tested.
Manner of motion verbs iterate a change in location fromstate to state.The value is assigned and reassigned.
Pustejovsky DITL
![Page 138: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/138.jpg)
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Event Structure
Directed Motion
(116)
x≠y?↶
loc(z) = x e1νÐ→ loc(z) = y e2
When this test references the ordinal values on a scale, C, thisbecomes a directed ν-transition (ν), e.g., x ≼ y , x ≽ y .
(117) ν =dfC?↶ei
νÐ→ ei+1
Pustejovsky DITL
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Event Structure
Directed Motion
(118)
x≠y?↶
loc(z) = x e1νÐ→ loc(z) = y e2
When this test references the ordinal values on a scale, C, thisbecomes a directed ν-transition (ν), e.g., x ≼ y , x ≽ y .
(119) ν =dfC?↶ei
νÐ→ ei+1
Pustejovsky DITL
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Event Structure
Directed Motion
(120)
x≠y?↶
loc(z) = x e1νÐ→ loc(z) = y e2
When this test references the ordinal values on a scale, C, thisbecomes a directed ν-transition (ν), e.g., x ≼ y , x ≽ y .
(121) ν =dfC?↶ei
νÐ→ ei+1
Pustejovsky DITL
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Event Structure
Directed Motion
(122) e[i,i+1]HH
HHH
�����
x ≼ y?↶e i1
-x ∶= ye i+12
A(z) = x A(z) = y
Pustejovsky DITL
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Event Structure
Change and Directed Motion
Manner-of-motion verbs introduce an assignment of a locationvalue:loc(x) ∶= y ; y ∶= z
Directed motion introduces a dimension that is measuredagainst:d(b, y) < d(b, z)Path verbs introduce a pair of tests:¬φ? . . . φ?
Pustejovsky DITL
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Event Structure
Change and Directed Motion
Manner-of-motion verbs introduce an assignment of a locationvalue:loc(x) ∶= y ; y ∶= z
Directed motion introduces a dimension that is measuredagainst:d(b, y) < d(b, z)
Path verbs introduce a pair of tests:¬φ? . . . φ?
Pustejovsky DITL
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Event Structure
Change and Directed Motion
Manner-of-motion verbs introduce an assignment of a locationvalue:loc(x) ∶= y ; y ∶= z
Directed motion introduces a dimension that is measuredagainst:d(b, y) < d(b, z)Path verbs introduce a pair of tests:¬φ? . . . φ?
Pustejovsky DITL
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Event Structure
Change and the Trail it Leaves
The execution of a change in the value to an attribute A foran object x leaves a trail, τ .
For motion, this trail is the created object of the path p whichthe mover travels on;
For creation predicates, this trail is the created object broughtabout by order-preserving transformations as executed in thedirected process above.
Pustejovsky DITL
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Event Structure
Change and the Trail it Leaves
The execution of a change in the value to an attribute A foran object x leaves a trail, τ .
For motion, this trail is the created object of the path p whichthe mover travels on;
For creation predicates, this trail is the created object broughtabout by order-preserving transformations as executed in thedirected process above.
Pustejovsky DITL
![Page 147: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/147.jpg)
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Event Structure
Change and the Trail it Leaves
The execution of a change in the value to an attribute A foran object x leaves a trail, τ .
For motion, this trail is the created object of the path p whichthe mover travels on;
For creation predicates, this trail is the created object broughtabout by order-preserving transformations as executed in thedirected process above.
Pustejovsky DITL
![Page 148: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/148.jpg)
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Event Structure
Change and the Trail it Leaves
The execution of a change in the value to an attribute A foran object x leaves a trail, τ .
For motion, this trail is the created object of the path p whichthe mover travels on;
For creation predicates, this trail is the created object broughtabout by order-preserving transformations as executed in thedirected process above.
Pustejovsky DITL
![Page 149: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/149.jpg)
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Event Structure
Motion Leaving a Trail
(123) Motion leaving a trail:a. Assign a value, y , to the location of the moving object, x .
loc(x) ∶= y
b. Name this value b (this will be the beginning of themovement);
b ∶= yc. Initiate a path p that is a list, starting at b;
p ∶= (b)d. Then, reassign the value of y to z , where y ≠ z
y ∶= z , y ≠ ze. Add the reassigned value of y to path p;
p ∶= (p, z)f. Kleene iterate steps (d) and (e).
Pustejovsky DITL
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Event Structure
Motion Leaving a Trail
(124) Motion leaving a trail:a. Assign a value, y , to the location of the moving object, x .
loc(x) ∶= yb. Name this value b (this will be the beginning of themovement);
b ∶= y
c. Initiate a path p that is a list, starting at b;p ∶= (b)
d. Then, reassign the value of y to z , where y ≠ zy ∶= z , y ≠ z
e. Add the reassigned value of y to path p;p ∶= (p, z)
f. Kleene iterate steps (d) and (e).
Pustejovsky DITL
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Event Structure
Motion Leaving a Trail
(125) Motion leaving a trail:a. Assign a value, y , to the location of the moving object, x .
loc(x) ∶= yb. Name this value b (this will be the beginning of themovement);
b ∶= yc. Initiate a path p that is a list, starting at b;
p ∶= (b)
d. Then, reassign the value of y to z , where y ≠ zy ∶= z , y ≠ z
e. Add the reassigned value of y to path p;p ∶= (p, z)
f. Kleene iterate steps (d) and (e).
Pustejovsky DITL
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Event Structure
Motion Leaving a Trail
(126) Motion leaving a trail:a. Assign a value, y , to the location of the moving object, x .
loc(x) ∶= yb. Name this value b (this will be the beginning of themovement);
b ∶= yc. Initiate a path p that is a list, starting at b;
p ∶= (b)d. Then, reassign the value of y to z , where y ≠ z
y ∶= z , y ≠ z
e. Add the reassigned value of y to path p;p ∶= (p, z)
f. Kleene iterate steps (d) and (e).
Pustejovsky DITL
![Page 153: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/153.jpg)
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Event Structure
Motion Leaving a Trail
(127) Motion leaving a trail:a. Assign a value, y , to the location of the moving object, x .
loc(x) ∶= yb. Name this value b (this will be the beginning of themovement);
b ∶= yc. Initiate a path p that is a list, starting at b;
p ∶= (b)d. Then, reassign the value of y to z , where y ≠ z
y ∶= z , y ≠ ze. Add the reassigned value of y to path p;
p ∶= (p, z)f. Kleene iterate steps (d) and (e).
Pustejovsky DITL
![Page 154: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/154.jpg)
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Event Structure
Motion Leaving a Trail
(128) Motion leaving a trail:a. Assign a value, y , to the location of the moving object, x .
loc(x) ∶= yb. Name this value b (this will be the beginning of themovement);
b ∶= yc. Initiate a path p that is a list, starting at b;
p ∶= (b)d. Then, reassign the value of y to z , where y ≠ z
y ∶= z , y ≠ ze. Add the reassigned value of y to path p;
p ∶= (p, z)f. Kleene iterate steps (d) and (e).
Pustejovsky DITL
![Page 155: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/155.jpg)
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Event Structure
Quantifying the Resulting Trail
l1@t1 l2@t2 l3@t3
p=(b,l2,l3)p=(b,l2)p=(b)
Figure: Directed Motion leaving a Trail
(129) a. The ball rolled 20 feet.∃p∃x[[roll(x ,p) ∧ ball(x) ∧ length(p) = [20, foot]]
b. John biked for 5 miles.∃p[[bike(j ,p) ∧ length(p) = [5,mile]]
Pustejovsky DITL
![Page 156: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/156.jpg)
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Event Structure
Quantifying the Resulting Trail
l1@t1 l2@t2 l3@t3
p=(b,l2,l3)p=(b,l2)p=(b)
Figure: Directed Motion leaving a Trail
(130) a. The ball rolled 20 feet.∃p∃x[[roll(x ,p) ∧ ball(x) ∧ length(p) = [20, foot]]
b. John biked for 5 miles.∃p[[bike(j ,p) ∧ length(p) = [5,mile]]
Pustejovsky DITL
![Page 157: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/157.jpg)
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Event Structure
Quantifying the Resulting Trail
l1@t1 l2@t2 l3@t3
p=(b,l2,l3)p=(b,l2)p=(b)
Figure: Directed Motion leaving a Trail
(131) a. The ball rolled 20 feet.∃p∃x[[roll(x ,p) ∧ ball(x) ∧ length(p) = [20, foot]]
b. John biked for 5 miles.∃p[[bike(j ,p) ∧ length(p) = [5,mile]]
Pustejovsky DITL
![Page 158: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/158.jpg)
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Event Structure
Generalizing the Path Metaphor
We generalize the Path Metaphor to the analysis of thecreation predicates.
We analyze creation predicates as predicates referencing twotypes of scales.
Pustejovsky DITL
![Page 159: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/159.jpg)
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Event Structure
Generalizing the Path Metaphor
We generalize the Path Metaphor to the analysis of thecreation predicates.
We analyze creation predicates as predicates referencing twotypes of scales.
Pustejovsky DITL
![Page 160: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/160.jpg)
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Event Structure
Type of Creation Verbs
(132) a. John wrote a letter.
b. Sophie wrote for hours.c. Sophie wrote for an hour.
(133) a. John built a wooden bookcase.b. *John built for weeks.
Pustejovsky DITL
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Event Structure
Type of Creation Verbs
(134) a. John wrote a letter.b. Sophie wrote for hours.
c. Sophie wrote for an hour.
(135) a. John built a wooden bookcase.b. *John built for weeks.
Pustejovsky DITL
![Page 162: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/162.jpg)
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Event Structure
Type of Creation Verbs
(136) a. John wrote a letter.b. Sophie wrote for hours.c. Sophie wrote for an hour.
(137) a. John built a wooden bookcase.b. *John built for weeks.
Pustejovsky DITL
![Page 163: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/163.jpg)
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Event Structure
Linguistic View on Scales
Some verbs expressing change are associated with a scalewhile others are not (scalar vs. non-scalar change).
There is a single scale domain (ordinal scale), which varieswith respect to mereological complexity (two-point vs.multi-point) and specificity of the end point (bounded vs.unbounded).
Scales are classified on the basis of the attribute beingmeasured:
PROPERTY SCALES: often found with change of state verbs.PATH SCALES: most often found with directed motion verbs.EXTENT SCALES: most often found with incremental themeverbs.
Pustejovsky DITL
![Page 164: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/164.jpg)
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Event Structure
Linguistic View on Scales
Some verbs expressing change are associated with a scalewhile others are not (scalar vs. non-scalar change).
There is a single scale domain (ordinal scale), which varieswith respect to mereological complexity (two-point vs.multi-point) and specificity of the end point (bounded vs.unbounded).
Scales are classified on the basis of the attribute beingmeasured:
PROPERTY SCALES: often found with change of state verbs.PATH SCALES: most often found with directed motion verbs.EXTENT SCALES: most often found with incremental themeverbs.
Pustejovsky DITL
![Page 165: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/165.jpg)
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Event Structure
Linguistic View on Scales
Some verbs expressing change are associated with a scalewhile others are not (scalar vs. non-scalar change).
There is a single scale domain (ordinal scale), which varieswith respect to mereological complexity (two-point vs.multi-point) and specificity of the end point (bounded vs.unbounded).
Scales are classified on the basis of the attribute beingmeasured:
PROPERTY SCALES: often found with change of state verbs.PATH SCALES: most often found with directed motion verbs.EXTENT SCALES: most often found with incremental themeverbs.
Pustejovsky DITL
![Page 166: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/166.jpg)
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Event Structure
Linguistic View on Scales
Some verbs expressing change are associated with a scalewhile others are not (scalar vs. non-scalar change).
There is a single scale domain (ordinal scale), which varieswith respect to mereological complexity (two-point vs.multi-point) and specificity of the end point (bounded vs.unbounded).
Scales are classified on the basis of the attribute beingmeasured:
PROPERTY SCALES: often found with change of state verbs.
PATH SCALES: most often found with directed motion verbs.EXTENT SCALES: most often found with incremental themeverbs.
Pustejovsky DITL
![Page 167: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/167.jpg)
59/78
Event Structure
Linguistic View on Scales
Some verbs expressing change are associated with a scalewhile others are not (scalar vs. non-scalar change).
There is a single scale domain (ordinal scale), which varieswith respect to mereological complexity (two-point vs.multi-point) and specificity of the end point (bounded vs.unbounded).
Scales are classified on the basis of the attribute beingmeasured:
PROPERTY SCALES: often found with change of state verbs.PATH SCALES: most often found with directed motion verbs.
EXTENT SCALES: most often found with incremental themeverbs.
Pustejovsky DITL
![Page 168: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/168.jpg)
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Event Structure
Linguistic View on Scales
Some verbs expressing change are associated with a scalewhile others are not (scalar vs. non-scalar change).
There is a single scale domain (ordinal scale), which varieswith respect to mereological complexity (two-point vs.multi-point) and specificity of the end point (bounded vs.unbounded).
Scales are classified on the basis of the attribute beingmeasured:
PROPERTY SCALES: often found with change of state verbs.PATH SCALES: most often found with directed motion verbs.EXTENT SCALES: most often found with incremental themeverbs.
Pustejovsky DITL
![Page 169: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/169.jpg)
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Event Structure
Linguistic View on Scales
Various scholars have observed that for certain scalarexpressions the scale appears not to be supplied by the verb.
For example, Rappaport Hovav 2008, Kennedy 2009 claimthat “the scale which occurs with incremental theme verbs(extent scale) is not directly encoded in the verb, but ratherprovided by the referent of the direct object”.
This has lead them to the assumption that when nominalreference plays a role in measuring the change, V is notassociated with a scale (denoting a non-scalar change).
Pustejovsky DITL
![Page 170: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/170.jpg)
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Event Structure
Linguistic View on Scales
Various scholars have observed that for certain scalarexpressions the scale appears not to be supplied by the verb.
For example, Rappaport Hovav 2008, Kennedy 2009 claimthat “the scale which occurs with incremental theme verbs(extent scale) is not directly encoded in the verb, but ratherprovided by the referent of the direct object”.
This has lead them to the assumption that when nominalreference plays a role in measuring the change, V is notassociated with a scale (denoting a non-scalar change).
Pustejovsky DITL
![Page 171: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/171.jpg)
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Event Structure
Linguistic View on Scales
Various scholars have observed that for certain scalarexpressions the scale appears not to be supplied by the verb.
For example, Rappaport Hovav 2008, Kennedy 2009 claimthat “the scale which occurs with incremental theme verbs(extent scale) is not directly encoded in the verb, but ratherprovided by the referent of the direct object”.
This has lead them to the assumption that when nominalreference plays a role in measuring the change, V is notassociated with a scale (denoting a non-scalar change).
Pustejovsky DITL
![Page 172: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/172.jpg)
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Event Structure
Challenge for Scalar Models
Identify the source(s) of the measure of change.
What is the basic classification of the predicate with respectto its scalar structure?
What is the exact contribution of each member of thelinguistic expression to the measurement of the change?
What is the role of nominal reference in aspectualcomposition?
Pustejovsky DITL
![Page 173: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/173.jpg)
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Event Structure
Challenge for Scalar Models
Identify the source(s) of the measure of change.
What is the basic classification of the predicate with respectto its scalar structure?
What is the exact contribution of each member of thelinguistic expression to the measurement of the change?
What is the role of nominal reference in aspectualcomposition?
Pustejovsky DITL
![Page 174: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/174.jpg)
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Event Structure
Challenge for Scalar Models
Identify the source(s) of the measure of change.
What is the basic classification of the predicate with respectto its scalar structure?
What is the exact contribution of each member of thelinguistic expression to the measurement of the change?
What is the role of nominal reference in aspectualcomposition?
Pustejovsky DITL
![Page 175: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/175.jpg)
61/78
Event Structure
Challenge for Scalar Models
Identify the source(s) of the measure of change.
What is the basic classification of the predicate with respectto its scalar structure?
What is the exact contribution of each member of thelinguistic expression to the measurement of the change?
What is the role of nominal reference in aspectualcomposition?
Pustejovsky DITL
![Page 176: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/176.jpg)
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Event Structure
How Language Encodes Scalar InformationPustejovsky and Jezek 2012
Verbs reference a specific scale.
We measure change according to this scale domain.
Scales are introduced by predication (encoded in a verb).
Scales can be introduced by composition (functionapplication).
Verbs may reference multiple scales.
Pustejovsky DITL
![Page 177: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/177.jpg)
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Event Structure
How Language Encodes Scalar InformationPustejovsky and Jezek 2012
Verbs reference a specific scale.
We measure change according to this scale domain.
Scales are introduced by predication (encoded in a verb).
Scales can be introduced by composition (functionapplication).
Verbs may reference multiple scales.
Pustejovsky DITL
![Page 178: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/178.jpg)
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Event Structure
How Language Encodes Scalar InformationPustejovsky and Jezek 2012
Verbs reference a specific scale.
We measure change according to this scale domain.
Scales are introduced by predication (encoded in a verb).
Scales can be introduced by composition (functionapplication).
Verbs may reference multiple scales.
Pustejovsky DITL
![Page 179: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/179.jpg)
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Event Structure
How Language Encodes Scalar InformationPustejovsky and Jezek 2012
Verbs reference a specific scale.
We measure change according to this scale domain.
Scales are introduced by predication (encoded in a verb).
Scales can be introduced by composition (functionapplication).
Verbs may reference multiple scales.
Pustejovsky DITL
![Page 180: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/180.jpg)
62/78
Event Structure
How Language Encodes Scalar InformationPustejovsky and Jezek 2012
Verbs reference a specific scale.
We measure change according to this scale domain.
Scales are introduced by predication (encoded in a verb).
Scales can be introduced by composition (functionapplication).
Verbs may reference multiple scales.
Pustejovsky DITL
![Page 181: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/181.jpg)
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Event Structure
Scale Theory: Stevens (1946), Krantz et al (1971)
Nominal scales: composed of sets of categories in whichobjects are classified;
Ordinal scales: indicate the order of the data according tosome criterion (a partial ordering over a defined domain).They tell nothing about the distance between units of thescale.
Interval scales: have equal distances between scale units andpermit statements to be made about those units as comparedto other units; there is no zero. Interval scales permit astatement of “more than” or “less than” but not of “howmany times more.”
Ratio scales: have equal distances between scale units as wellas a zero value. Most measures encountered in daily discourseare based on a ratio scale.
Pustejovsky DITL
![Page 182: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/182.jpg)
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Event Structure
Scale Theory: Stevens (1946), Krantz et al (1971)
Nominal scales: composed of sets of categories in whichobjects are classified;
Ordinal scales: indicate the order of the data according tosome criterion (a partial ordering over a defined domain).They tell nothing about the distance between units of thescale.
Interval scales: have equal distances between scale units andpermit statements to be made about those units as comparedto other units; there is no zero. Interval scales permit astatement of “more than” or “less than” but not of “howmany times more.”
Ratio scales: have equal distances between scale units as wellas a zero value. Most measures encountered in daily discourseare based on a ratio scale.
Pustejovsky DITL
![Page 183: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/183.jpg)
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Event Structure
Scale Theory: Stevens (1946), Krantz et al (1971)
Nominal scales: composed of sets of categories in whichobjects are classified;
Ordinal scales: indicate the order of the data according tosome criterion (a partial ordering over a defined domain).They tell nothing about the distance between units of thescale.
Interval scales: have equal distances between scale units andpermit statements to be made about those units as comparedto other units; there is no zero. Interval scales permit astatement of “more than” or “less than” but not of “howmany times more.”
Ratio scales: have equal distances between scale units as wellas a zero value. Most measures encountered in daily discourseare based on a ratio scale.
Pustejovsky DITL
![Page 184: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/184.jpg)
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Event Structure
Scale Theory: Stevens (1946), Krantz et al (1971)
Nominal scales: composed of sets of categories in whichobjects are classified;
Ordinal scales: indicate the order of the data according tosome criterion (a partial ordering over a defined domain).They tell nothing about the distance between units of thescale.
Interval scales: have equal distances between scale units andpermit statements to be made about those units as comparedto other units; there is no zero. Interval scales permit astatement of “more than” or “less than” but not of “howmany times more.”
Ratio scales: have equal distances between scale units as wellas a zero value. Most measures encountered in daily discourseare based on a ratio scale.
Pustejovsky DITL
![Page 185: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/185.jpg)
63/78
Event Structure
Scale Theory: Stevens (1946), Krantz et al (1971)
Nominal scales: composed of sets of categories in whichobjects are classified;
Ordinal scales: indicate the order of the data according tosome criterion (a partial ordering over a defined domain).They tell nothing about the distance between units of thescale.
Interval scales: have equal distances between scale units andpermit statements to be made about those units as comparedto other units; there is no zero. Interval scales permit astatement of “more than” or “less than” but not of “howmany times more.”
Ratio scales: have equal distances between scale units as wellas a zero value. Most measures encountered in daily discourseare based on a ratio scale.
Pustejovsky DITL
![Page 186: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/186.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Use multiple scalar domains and the “change as program”metaphor proposed in Dynamic Interval Temporal Logic(DITL, Pustejovsky 2011, Pustejovsky & Moszkowicz 2011).
Define change as a transformation of state (cf. Galton, 2000,Naumann 2001) involving two possible kinds of result,depending on the change program which is executed:
If the program is “change by testing”, Result refers to thecurrent value of the attribute after an event (e.g., the housein build a house, the apple in eat an apple, etc.).
If the program is “change by assignment”, Result refers to therecord or trail of the change (e.g., the path of a walking, thestuff written in writing, etc.).
Pustejovsky DITL
![Page 187: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/187.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Use multiple scalar domains and the “change as program”metaphor proposed in Dynamic Interval Temporal Logic(DITL, Pustejovsky 2011, Pustejovsky & Moszkowicz 2011).
Define change as a transformation of state (cf. Galton, 2000,Naumann 2001) involving two possible kinds of result,depending on the change program which is executed:
If the program is “change by testing”, Result refers to thecurrent value of the attribute after an event (e.g., the housein build a house, the apple in eat an apple, etc.).
If the program is “change by assignment”, Result refers to therecord or trail of the change (e.g., the path of a walking, thestuff written in writing, etc.).
Pustejovsky DITL
![Page 188: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/188.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Use multiple scalar domains and the “change as program”metaphor proposed in Dynamic Interval Temporal Logic(DITL, Pustejovsky 2011, Pustejovsky & Moszkowicz 2011).
Define change as a transformation of state (cf. Galton, 2000,Naumann 2001) involving two possible kinds of result,depending on the change program which is executed:
If the program is “change by testing”, Result refers to thecurrent value of the attribute after an event (e.g., the housein build a house, the apple in eat an apple, etc.).
If the program is “change by assignment”, Result refers to therecord or trail of the change (e.g., the path of a walking, thestuff written in writing, etc.).
Pustejovsky DITL
![Page 189: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/189.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Use multiple scalar domains and the “change as program”metaphor proposed in Dynamic Interval Temporal Logic(DITL, Pustejovsky 2011, Pustejovsky & Moszkowicz 2011).
Define change as a transformation of state (cf. Galton, 2000,Naumann 2001) involving two possible kinds of result,depending on the change program which is executed:
If the program is “change by testing”, Result refers to thecurrent value of the attribute after an event (e.g., the housein build a house, the apple in eat an apple, etc.).
If the program is “change by assignment”, Result refers to therecord or trail of the change (e.g., the path of a walking, thestuff written in writing, etc.).
Pustejovsky DITL
![Page 190: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/190.jpg)
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Event Structure
Scale shiftingPustejovsky and Jezek 2012
Scale Shifting is mapping from one scalar domain to anotherscalar domain.ordinal ⇒ nominalnominal ⇒ ordinalordinal ⇒ interval. . .
Scale Shifting may be triggered by:
Adjuncts: for/in adverbials, degree modifiers, resultativephrases, etc.
Arguments (selected vs. non-selected, semantic typing,quantification).
Pustejovsky DITL
![Page 191: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/191.jpg)
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Event Structure
Scale shiftingPustejovsky and Jezek 2012
Scale Shifting is mapping from one scalar domain to anotherscalar domain.ordinal ⇒ nominalnominal ⇒ ordinalordinal ⇒ interval. . .
Scale Shifting may be triggered by:
Adjuncts: for/in adverbials, degree modifiers, resultativephrases, etc.
Arguments (selected vs. non-selected, semantic typing,quantification).
Pustejovsky DITL
![Page 192: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/192.jpg)
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Event Structure
Scale shiftingPustejovsky and Jezek 2012
Scale Shifting is mapping from one scalar domain to anotherscalar domain.ordinal ⇒ nominalnominal ⇒ ordinalordinal ⇒ interval. . .
Scale Shifting may be triggered by:
Adjuncts: for/in adverbials, degree modifiers, resultativephrases, etc.
Arguments (selected vs. non-selected, semantic typing,quantification).
Pustejovsky DITL
![Page 193: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/193.jpg)
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Event Structure
Scale shiftingPustejovsky and Jezek 2012
Scale Shifting is mapping from one scalar domain to anotherscalar domain.ordinal ⇒ nominalnominal ⇒ ordinalordinal ⇒ interval. . .
Scale Shifting may be triggered by:
Adjuncts: for/in adverbials, degree modifiers, resultativephrases, etc.
Arguments (selected vs. non-selected, semantic typing,quantification).
Pustejovsky DITL
![Page 194: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/194.jpg)
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Event Structure
Scale shiftingPustejovsky and Jezek 2012
Scale Shifting is mapping from one scalar domain to anotherscalar domain.ordinal ⇒ nominalnominal ⇒ ordinalordinal ⇒ interval. . .
Scale Shifting may be triggered by:
Adjuncts: for/in adverbials, degree modifiers, resultativephrases, etc.
Arguments (selected vs. non-selected, semantic typing,quantification).
Pustejovsky DITL
![Page 195: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/195.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Accomplishments are Lexically Encoded Tests.John built a house.
Test-predicates for creation verbs
build selects for a quantized individual as argument.
λzλyλx[build(x , z , y)]
An ordinal scale drives the incremental creation forward
A nominal scale acts as a test for completion (telicity)
Pustejovsky DITL
![Page 196: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/196.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Accomplishments are Lexically Encoded Tests.
John built a house.
Test-predicates for creation verbs
build selects for a quantized individual as argument.
λzλyλx[build(x , z , y)]
An ordinal scale drives the incremental creation forward
A nominal scale acts as a test for completion (telicity)
Pustejovsky DITL
![Page 197: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/197.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Accomplishments are Lexically Encoded Tests.John built a house.
Test-predicates for creation verbs
build selects for a quantized individual as argument.
λzλyλx[build(x , z , y)]
An ordinal scale drives the incremental creation forward
A nominal scale acts as a test for completion (telicity)
Pustejovsky DITL
![Page 198: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/198.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Accomplishments are Lexically Encoded Tests.John built a house.
Test-predicates for creation verbs
build selects for a quantized individual as argument.
λzλyλx[build(x , z , y)]
An ordinal scale drives the incremental creation forward
A nominal scale acts as a test for completion (telicity)
Pustejovsky DITL
![Page 199: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/199.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Accomplishments are Lexically Encoded Tests.John built a house.
Test-predicates for creation verbs
build selects for a quantized individual as argument.
λzλyλx[build(x , z , y)]
An ordinal scale drives the incremental creation forward
A nominal scale acts as a test for completion (telicity)
Pustejovsky DITL
![Page 200: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/200.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Accomplishments are Lexically Encoded Tests.John built a house.
Test-predicates for creation verbs
build selects for a quantized individual as argument.
λzλyλx[build(x , z , y)]
An ordinal scale drives the incremental creation forward
A nominal scale acts as a test for completion (telicity)
Pustejovsky DITL
![Page 201: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/201.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Accomplishments are Lexically Encoded Tests.John built a house.
Test-predicates for creation verbs
build selects for a quantized individual as argument.
λzλyλx[build(x , z , y)]
An ordinal scale drives the incremental creation forward
A nominal scale acts as a test for completion (telicity)
Pustejovsky DITL
![Page 202: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/202.jpg)
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Event Structure
Generalizing the Path Metaphor to Creation PredicatesPustejovsky and Jezek 2012
Accomplishments are Lexically Encoded Tests.John built a house.
Test-predicates for creation verbs
build selects for a quantized individual as argument.
λzλyλx[build(x , z , y)]
An ordinal scale drives the incremental creation forward
A nominal scale acts as a test for completion (telicity)
Pustejovsky DITL
![Page 203: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/203.jpg)
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Event Structure
Incremental Theme and Parallel Scales
A B C D E
Mary is building a table.
Change is measured over an ordinal scale.
Trail, τ is null.
Pustejovsky DITL
![Page 204: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/204.jpg)
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Event Structure
Incremental Theme and Parallel Scales
AB C D E
Mary is building a table.
Change is measured over an ordinal scale.
Trail, τ = [A].
Pustejovsky DITL
![Page 205: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/205.jpg)
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Event Structure
Incremental Theme and Parallel Scales
A BC D E
Mary is building a table.
Change is measured over an ordinal scale.
Trail, τ = [A,B]
Pustejovsky DITL
![Page 206: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/206.jpg)
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Event Structure
Incremental Theme and Parallel Scales
A BC
D E
Mary is building a table.
Change is measured over an ordinal scale.
Trail, τ = [A,B,C ]
Pustejovsky DITL
![Page 207: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/207.jpg)
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Event Structure
Incremental Theme and Parallel Scales
A BC D
E
Mary is building a table.
Change is measured over an ordinal scale.
Trail, τ = [A,B,C ,D]
Pustejovsky DITL
![Page 208: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/208.jpg)
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Event Structure
Incremental Theme and Parallel Scales
A BC D
E
Mary built a table.
Change is measured over a nominal scale.
Trail, τ = [A,B,C ,D,E ]; table(τ).
Pustejovsky DITL
![Page 209: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/209.jpg)
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Event Structure
Accomplishments
(138) a. John built a table.b. Mary walked to the store.
build(x , z , y) build(x , z , y)+ build(x , z , y), y = v¬table(v) table(v) ⟨i,j⟩
Table: Accomplishment: parallel tracks of changes
Pustejovsky DITL
![Page 210: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/210.jpg)
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Event Structure
Dynamic Event Structure
(139) eHH
HHH
�����
e1 -αe2
¬φ?
φ?↶
-
φ
HHHHH
�����
e11-α
e12 . . . -α e1k
Pustejovsky DITL
![Page 211: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/211.jpg)
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Event Structure
Parallel Scales define an Accomplishment
(140) eHH
HHH
�����
e1 -build e2
¬table?
table?↶
-
table(v)
HHHHH
�����
e11-builde12 . . . -build e1k
Pustejovsky DITL
![Page 212: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/212.jpg)
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Event Structure
Lab on identification of event type properties in corpora
Choose three target verbs. State your hypothesis regardingthe event type associated with the verbs wrt to the extendedin time vs instantaneous dimension.
Count the co-occurrences of the verbs in the raws of thematrix with the expressions in the columns in the BNC usingthe SkE.
You can use the context search - setting the window, or refineyour search with CQL or Word Sketches.
Pustejovsky DITL
![Page 213: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/213.jpg)
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Event Structure
Lab on detection of event type properties in corpora
For the ”for x time” expressions, you can use the followingregular expression:[lemma = ”for”][]{0,1}[lemma =”instant ∣second ∣minute ∣hour ∣day ∣week ∣month∣year”]Fill the cooccurrence counts in last column of the matrix andrank the verbs accordingly.
Select 1 concordance for each verb which constitutes anexample of event-type shiftings in context.
Summarize your results.
Pustejovsky DITL
![Page 214: Dynamic Interval Temporal Logic - CS 135 · Dynamic Interval Temporal Logic James Pustejovsky Brandeis University November 17, 2017 Pustejovsky DITL. 1/78 Event Structure Lecture](https://reader030.vdocuments.site/reader030/viewer/2022041021/5ed200463dafc82aeb3aa24a/html5/thumbnails/214.jpg)
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Event Structure
Lab on detection of event type properties in corpora
Co-occurrence matrixes detecting extended vs. instantaneousevents
suddenly still for x time finishV total occ
happenoccur
breatheappear
diesleepwalkrun
laughwake up
falldevelopwatchfreeze
Ongoing work E. Jezek, M. Sadrzadeh and E. Ponti (unpublished).
Pustejovsky DITL